通过图像分析植物卷须形变，揭示其触觉响应机制
Image-based Morphological Characterization of Filamentous Biological Structures with Non-constant Curvature Shape Feature
- 用分段螺旋线模型重建卷须受力后三维形态
- 模型准确率R2 > 0.99，显著优于深度学习方法
- 发现顶端对刺激更敏感，适合仿生机器人研究
卷须通过卷曲形状固定植物并向上生长以获取光照。尽管攀援植物已被长期研究，但提取其形态随时间变化、触发事件及接触位置间关系仍具挑战。为此，我们提出一种基于图像的方法，可分析卷须在不同部位受机械刺激后的形变过程。采用基于3D分段螺旋线的几何建模方法，重建卷须受摩擦刺激后的构型，重建结果具有高鲁棒性与可靠性，准确率R2 > 0.99。该方法相较深度学习方法具有更低数据需求、更少计算开销和更强可解释性。分析表明卷须顶端区域响应更强烈，可能与其组织更高的敏感性与柔韧性相关。本研究为理解植物生物力学提供了新方法，并为设计仿攀援植物的智能机器人系统奠定基础。
原文摘要 · Abstract (English)
Tendrils coil their shape to anchor the plant to supporting structures, allowing vertical growth toward light. Although climbing plants have been studied for a long time, extracting information regarding the relationship between the temporal shape change, the event that triggers it, and the contact location is still challenging. To help build this relation, we propose an image-based method by which it is possible to analyze shape changes over time in tendrils when mechano-stimulated in different portions of their body. We employ a geometric approach using a 3D Piece-Wise Clothoid-based model to reconstruct the configuration taken by a tendril after mechanical rubbing. The reconstruction shows high robustness and reliability with an accuracy of R2 > 0.99. This method demonstrates distinct advantages over deep learning-based approaches, including reduced data requirements, lower computational costs, and interpretability. Our analysis reveals higher responsiveness in the apical segment of tendrils, which might correspond to higher sensitivity and tissue flexibility in that region of the organs. Our study provides a methodology for gaining new insights into plant biomechanics and offers a foundation for designing and developing novel intelligent robotic systems inspired by climbing plants.
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